# Learning Data Analytics Moves Beyond Vanity Metrics, Says Industry Expert
John Cleave outlines a practical framework for L&D leaders seeking to move past surface-level learning metrics and toward genuine measurement of learning impact.
The conversation centers on a persistent problem in corporate training. Most organizations track attendance, completion rates, and course enrollments. These numbers feel safe and easy to report upward. They answer simple questions: Did people show up? Did they finish? But they reveal nothing about whether learning actually changed behavior or improved performance.
Cleave's approach separates learning analytics into two distinct categories. One measures activity: how many people took a course, how long they spent, how often they clicked through modules. The other measures outcome: did learners apply new skills on the job, did productivity increase, did error rates drop.
The distinction matters because L&D budgets depend on demonstrating value. When a training leader reports that 500 employees completed compliance training, executives ask the next logical question: So what? Did that training prevent violations? Did it reduce liability? Activity metrics alone cannot answer those questions.
Cleave recommends building measurement systems around business goals rather than training delivery. If the organization wants to reduce customer churn, the metric should track whether trained employees actually retain more customers. If the goal is faster onboarding, measurement should show time-to-productivity for trained versus untrained new hires. If safety matters, incident rates matter more than course completion rates.
This shift requires different data sources. Activity data lives in learning management systems and typically flows automatically. Outcome data requires connecting learning platforms to human resources systems, performance management tools, customer relationship software, and operational dashboards. That integration work takes time and technical expertise most L&D teams lack.
The conversation also addresses a common objection: some learning impact resists easy measurement. A course on leadership philosophy or creative thinking produces results that emerge over months or years, not weeks. Standardized testing works poorly for evaluating whether someone became a better strategic thinker. Cleave acknowledges this reality while arguing that imperfect measurement beats no measurement.
He suggests starting with high-stakes training where impact is most visible and measurable. Sales training shows results quickly through revenue data. Customer service training shows up in satisfaction scores. Technical certifications show up in support ticket resolution times. These domains offer clearer cause-and-effect chains than softer skills development.
The framework also includes forward-looking analytics. Rather than measuring only what happened after training, predictive models can identify which employees are most likely to apply new learning, which teams show the strongest adoption patterns, and which training formats drive the best outcomes. This intelligence helps L&D teams refine future programs before rolling them out organization-wide.
Cleave emphasizes that this work requires collaboration across departments. L&D cannot measure impact alone. Finance teams must provide performance data. Operations teams must identify what success looks like. Human resources teams must grant access to employee records. When these departments align around learning goals, measurement becomes possible.
The broader implication is that L&D transitions from a support function that delivers courses to a strategic partner that drives business results. That shift demands rigor in data collection, honesty about what learning can and cannot accomplish, and willingness to sunset programs that show no measurable benefit.